code: def _produce_train_output(self, input_batch: Tuple) -> torch.Tensor:
      (
          past_target,
          historic_future_covariates,
          future_covariates,
          static_covariates,
      ) = input_batch
      # For the RNN we concatenate the past_target with the future_covariates
      # (they have the same length because we enforce a Shift dataset for RNNs)
      model_input = (
          torch.cat([past_target, future_covariates], dim=2)
          if future_covariates is not None
          else past_target,
          static_covariates,
      )
      return self(model_input)[0]
  
imports: import inspect
  from abc import ABC, abstractmethod
  from typing import Optional, Sequence, Tuple, Type, Union
  import torch
  import torch.nn
  from darts.logging import get_logger, raise_if_not, raise_log
  from darts.models.forecasting.pl_forecasting_module import PLDualCovariatesModule, io_processor
  from darts.models.forecasting.torch_forecasting_model import DualCovariatesTorchModel
  from darts.timeseries import TimeSeries
  from darts.utils.data import DualCovariatesShiftedDataset, TrainingDataset
label_desc: Use image node_id 3 for calling the CustomRNNModule obj's underlying member method code with example usage: obj._produce_train_output(input_batch) and returns: self